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Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.
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PR4E Course Page: https://www.coursera.org/course/pythonlearn
In this video we meet the grader for the Python assignments in the Programming for Everybody (#PR4E) course from the University of Michigan School of Information on Coursera.

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Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.
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Get the 12-hour course for just $19 at https://www.udemy.com/data-science-and-machine-learning-with-python-hands-on/?couponCode=DATATUBE
New! Updated for TensorFlow 1.10
Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That's just the average! And it's not just about money - it's interesting work too!
If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path. This comprehensive machine learning tutorial includes over 80 lectures spanning 12 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice. I’ll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn’t.
Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won't find academic, deeply mathematical coverage of these algorithms in this course - the focus is on practical understanding and application of them. At the end, you'll be given a final project to apply what you've learned!
The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. We'll cover the machine learning, AI, and data mining techniques real employers are looking for, including:
Deep Learning / Neural Networks (MLP's, CNN's, RNN's) with TensorFlow and Keras
Sentiment analysis
Image recognition and classification
Regression analysis
K-Means Clustering
Principal Component Analysis
Train/Test and cross validation
Bayesian Methods
Decision Trees and Random Forests
Multivariate Regression
Multi-Level Models
Support Vector Machines
Reinforcement Learning
Collaborative Filtering
K-Nearest Neighbor
Bias/Variance Tradeoff
Ensemble Learning
Term Frequency / Inverse Document Frequency
Experimental Design and A/B Tests
...and much more! There's also an entire section on machine learning with Apache Spark, which lets you scale up these techniques to "big data" analyzed on a computing cluster. And you'll also get access to this course's Facebook Group, where you can stay in touch with your classmates.
If you're new to Python, don't worry - the course starts with a crash course. If you've done some programming before, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC's; the sample code will also run on MacOS or Linux desktop systems, but I can't provide OS-specific support for them.
If you’re a programmer looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic techniques used by real-world industry data scientists. These are topics any successful technologist absolutely needs to know about, so what are you waiting for? Enroll now!
"I started doing your course in 2015... Eventually I got interested and never thought that I will be working for corporate before a friend offered me this job. I am learning a lot which was impossible to learn in academia and enjoying it thoroughly. To me, your course is the one that helped me understand how to work with corporate problems. How to think to be a success in corporate AI research. I find you the most impressive instructor in ML, simple yet convincing." - Kanad Basu, PhD

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Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.
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Learn how machine learning is used to optimize the beer manufacturing process. This use case has a direct impact on the production line and identifying downtime of equipment, and huge impact on cost, time, and quality of beer being produced. The role of machine learning is to improve the manufacturing process and quality while driving higher ROI through an undisruptive production process.
Listen to experts on how Deep Learning was used with classifications of good parts vs. bad parts using TensorFlow. The model will be deployed to Google Cloud Machine Learning Engine where it will make predictions of the new data that is fed every day with an interactive dashboard using Data Studio.
Event schedule → http://g.co/next18
Watch more Machine Learning & AI sessions here → http://bit.ly/2zGKfcg
Next ‘18 All Sessions playlist → http://bit.ly/Allsessions
Subscribe to the Google Cloud channel! → http://bit.ly/NextSub

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Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.
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On November 8, 2017, John Hart and Adam Fein from the University of Illinois at Urbana-Champaign visited Columbia for a conversation about how the land-grant research university is embracing online education. Three years ago, UIUC launched an online MBA program in partnership with Coursera. Since then, UIUC added two other online degree programs: Master’s of Computer Science in Data Science and the Master’s of Accounting.
John Hart is a Professor of Computer Science and Executive Associate Dean of the Graduate College at the University of Illinois at Urbana-Champaign. Full bio: https://cs.illinois.edu/directory/profile/jch
Adam Fein is the Assistant Provost for Educational Innovation at the University of Illinois at Urbana-Champaign. Full bio: https://provost.illinois.edu/staff-directory/fein-adam/
The Provost’s Conversations on Online Learning (PCoOL) is a series of public talks by leading experts and peers on the future of education, specifically around online education. View all the Conversations on Online Learning to date on YouTube: https://www.youtube.com/watch?list=SPSuwqsAnJMtzPf-cyP3ZhKjT4LodQ3x_L&v=lBWmGkem3TE
You can also view all past seminars and conversations at Columbia University on topics related to online learning on the Columbia Online website: https://online.columbia.edu/seminars/
Subscribe to Columbia Learn: https://www.youtube.com/ccnmtl
View our full video catalog: https://www.youtube.com/user/CCNMTL/playlists

Stanford professors Rita Popat and Kristin Sainani introduce the new graduate certificate in Epidemiology and Clinical Research (https://stanford.io/2MLDKqV), now offered by the Epidemiology division of the Health Research and Policy Department at Stanford School of Medicine.
In this session, professors Popat and Sainani:
-Introduce current research and emerging trends in the field
-Discuss classes in this program, expected outcomes and required skills
-Respond to audience questions

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Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.
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Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.
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Slides from this presentation are available at: http://www.slideshare.net/alexandrapickett/cote-summitt-gveletsianos
The growing need for an educated workforce, changing student demographics, opportunities presented by new technologies, and increases in the cost of attending post-graduate educational institutions have led many educators, policymakers, and businesspeople to seek more affordable models of educating large numbers of students, such as open textbooks and Massive Open Online Courses (MOOCs). An uncertain job market, expanding opportunities to interact with diverse audiences in online settings, and the potential of online networks to increase citations and impact have also led many academics to engage in open scholarship and make use of such online social networks as Twitter and Academia.edu. Common to both these developments is an increasing advocacy for and engagement with open practices in teaching, learning, and scholarship. In this talk, I will describe a number of emerging online practices and share results from my research into these practices.

Google Tech Talk
November 19, 2012
(click on "show more")
Presented by Joseph Jay Williams
ABSTRACT
Recent research in Cognitive Science provides insights into how learning can be improved that are complementary to those gained from practical experience and the research in Computer Science, Education and other Learning Sciences. This talk considers how learning can be improved by: (1) Asking questions and requesting explanations; (2) Presenting specific examples to illustrate abstract principles; (3) Using tests as pedagogical rather than assessment tools. Moreover, online education provides the unique opportunity for hybrid research that is simultaneously applied and academic. Online environments satisfy the scientific requirements of randomized experiments and precise control, as well as the practical need for ecological validity, fidelity, and scalable dissemination. One virtue of a basic Cognitive Science approach to online education is revealing abstract similarities in learning different topics: In addition to presenting ongoing research at Khan Academy and MOOCs like EdX, I discuss how analogous principles can be explored in teaching end-users Google Power Search, internal training, and customer education. This work can therefore simultaneously advance public education and yield corporate benefits.
Presenter's Biography:
Joseph Jay Williams does Cognitive Science research on how generating explanations promotes learning, and Online Education work on improving learning from mathematics exercises (Khan Academy), increasing motivation to learn by changing people's beliefs about intelligence (Project for Education Research that Scales: www.perts.net), teaching metacognitive & learning strategies in Massive Open Online Courses (EdX), and using technology to change educational and health habits. He is finishing his PhD in Psychology at UC Berkeley, and also has interests in consulting for corporate e-learning and training, web development for online education, using journalism to disseminate research to practitioners, and education in online search and problem-solving for students and entrepreneurs.
For resources on Cognitive Science & Online Education see: www.josephjaywilliams.com/education, sites.cognitivescience.co/learn, or www.learningresearch.net

August 11, 2016 talk @Phosphorus: Dipping into Guacamole — a Spark-powered Somatic Variant Caller. Presented by Tim O'Donnell from Hammer Lab at Mount Sinai.
Next generation sequencing of tumor DNA and RNA has revolutionized cancer genomics, and projects such as the Cancer Genome Atlas have sequenced over ten thousand patient samples. Detecting cancer mutations from paired tumor/normal sequencing is more challenging than traditional germline variant calling because tumors are heterogeneous mixtures of cancer clones.
In this talk, Tim O'Donnell and Ryan Williams from Hammer Lab (a lab within the Icahn Institute at Mount Sinai) walked through development progress on Guacamole, a somatic variant caller (which helps identify DNA mutations from Next Generation Sequencing data) that combines evidence from multiple DNA or RNA samples from the same patient for better sensitivity.